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A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle

  • Xinyu Zhang
  • Mo Zhou
  • Huaping LiuEmail author
  • Amir Hussain
Article
  • 32 Downloads

Abstract

This paper introduces the functional system architecture of the Mengshi intelligent vehicle, winner of the 2018 World Intelligent Driving Challenge (WIDC). Different from traditional smart vehicles, a cognitive module is introduced in the system architecture to realise the transition from perception to decision-making. This is shown to enhance the practical utility of the smart vehicle, enabling safe and robust driving in different scenes. The collaborative work of hardware and software systems is achieved through multi-sensor fusion and artificial intelligence (AI) technologies, including novel use of deep machine learning and context-aware scene analysis to select optimal driving strategies. Experimental results using both robustness tests and road tests confirm that the Mengshi intelligent vehicle is reliable and robust in challenging environments. This paper describes the major components of this cognitively inspired architecture and discusses the results of the 2018 WIDC.

Keywords

Cognitive vehicle Autonomous driving Driving scene construction Cross-modal transfer 

Notes

Funding Information

This work was supported by the National High Technology Research and Development Program (“973”Program) of China under Grant No. 2016YFB0100903, National High Technology Research and Development Program of China under Grant No. 2018YFE0204300, Beijing Municipal Science and Technology Commission special major under Grant No. D171100005017002, National Natural Science Foundation of China under Grant No. U1664263.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Automotive Safety and EnergyTsinghua UniversityBeijingChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.School of ComputingEdinburgh Napier UniversityEdinburghUK

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